Learn Python Coding

前往频道在 Telegram

Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho

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网络:Machine Learning with Python 印度9 923 技术与应用3 407...

📈 Telegram 频道 Learn Python Coding 的分析概览

频道 Learn Python Coding (@pythonre) 英语语言赛道中的是活跃参与者。目前社区聚集了 39 481 名订阅者，在 技术与应用 类别中位列第 3 407，并在印度地区排名第 9 923 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 39 481 名订阅者。

根据 05 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 345，过去 24 小时变化为 10，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 1.23%。内容发布后 24 小时内通常能获得 1.09% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 484 次浏览，首日通常累积 430 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 2。
主题关注点： 内容集中在 math, harvard, oxford, supervision, waybienad 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho”

凭借高频更新（最新数据采集于 06 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

39 481

订阅者

+1024 小时

+727 天

+34530 天

484

帖子浏览量

~ 43024 小时

~ 59848 小时

1.23%

参与率

~ 4

每日帖子数

Ads index

beta

帖子存档

39 480

AI for Games, 3d edition Invite your friends 🌹🌹 @DataScience_Books

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AI for Games, 3d edition 👇👇👇👇👇

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Probabilistic two-stage detection Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network. github: https://github.com/xingyizhou/CenterNet2?utm_source=catalyzex.com paper: https://arxiv.org/pdf/2103.07461.pdf Invite your friends 🌹🌹 @DataScience_Books

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A curated list of awesome Python frameworks, libraries, software and resources. github: https://github.com/vinta/awesome-python Invite your friends 🌹🌹 @DataScience_Books

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Optimal transport in multilayer networks Github: https://github.com/cdebacco/MultiOT Paper: https://arxiv.org/abs/2106.07202v1 Invite your friends 🌹🌹 @DataScience_Books

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Part-aware Panoptic Segmentation Github: https://github.com/pmeletis/panoptic_parts Paper: https://arxiv.org/abs/2106.06351v1 Docs: https://panoptic-parts.readthedocs.io/en/stable Invite your friends 🌹🌹 @DataScience_Books

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Color2Style: Real-Time Exemplar-Based Image Colorization with Self-Reference Learning and Deep Feature Modulation ArXiV: https://arxiv.org/pdf/2106.08017.pdf Invite your friends 🌹🌹 @DataScience_Books

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Python machine learning from scratch Invite your friends 🌹🌹 @DataScience_Books

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✅ Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs Github: https://github.com/seongjunyun/Graph_Transformer_Networks Paper: https://arxiv.org/abs/2106.06218v1 Dataset: https://github.com/Jhy1993/HAN Invite your friends 🌹🌹 @DataScience_Books

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🧩 A Bayesian Analysis of Lego Prices in Python with PyMC3 https://austinrochford.com/posts/2021-06-10-lego-pymc3.html Lego Price Analysis: https://austinrochford.com/posts/2021-06-03-vader-meditation.html Invite your friends 🌹🌹 @DataScience_Books

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A Deep Variational Approach to Clustering Survival Data Github: https://github.com/i6092467/vadesc Paper: https://arxiv.org/abs/2106.05763v1 Invite your friends 🌹🌹 @DataScience_Books

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Pivotal Tuning for Latent-based Editing of Real Images Github: https://github.com/Talegqz/unsupervised_co_part_segmentation Paper: https://arxiv.org/abs/2106.05897v1 Invite your friends 🌹🌹 @DataScience_Books

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Microsoft's FLAML - Fast and Lightweight AutoML Github: https://github.com/microsoft/FLAML Code: https://github.com/microsoft/FLAML/tree/main/notebook/ Paper: https://arxiv.org/abs/2106.04815v1 Invite your friends 🌹🌹 @DataScience_Books

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Probabilistic Deep Learning (2020) Invite your friends 🌹🌹 @DataScience_Books

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GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings Github: https://github.com/rusty1s/pyg_autoscale Paper: https://arxiv.org/abs/2106.05609v1 Invite your friends 🌹🌹 @DataScience_Books

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